Inference for the difference in the area under the ROC curve derived from nested binary regression models.

Content Provider	Europe PMC
Author	Heller, Glenn Seshan, Venkatraman E. Moskowitz, Chaya S. Gönen, Mithat
Copyright Year	2016
Abstract	Summary The area under the curve (AUC) statistic is a common measure of model performance in a binary regression model. Nested models are used to ascertain whether the AUC statistic increases when new factors enter the model. The regression coefficient estimates used in the AUC statistics are computed using the maximum rank correlation methodology. Typically, inference for the difference in AUC statistics from nested models is derived under asymptotic normality. In this work, it is demonstrated that the asymptotic normality is true only when at least one of the new factors is associated with the binary outcome. When none of the new factors are associated with the binary outcome, the asymptotic distribution for the difference in AUC statistics is a linear combination of chi-square random variables. Further, when at least one new factor is associated with the outcome and the population difference is small, a variance stabilizing reparameterization improves the asymptotic normality of the AUC difference statistic. A confidence interval using this reparameterization is developed and simulations are generated to determine their coverage properties. The derived confidence interval provides information on the magnitude of the added value of new factors and enables investigators to weigh the size of the improvement against potential costs associated with the new factors. A pancreatic cancer data example is used to illustrate this approach.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5965312&blobtype=pdf
Page Count	15
ISSN	14654644
Volume Number	18
DOI	10.1093/biostatistics/kxw045
PubMed Central reference number	PMC5965312
Issue Number	2
PubMed reference number	27655817
Journal	Biostatistics (Oxford, England) [Biostatistics]
e-ISSN	14684357
Language	English
Publisher	Oxford University Press
Publisher Date	2017-04-01
Access Restriction	Open
Rights License	© The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Subject Keyword	Area under the receiver operating characteristic curve Confidence interval Incremental value Maximum rank correlation Nested models Risk classification model
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Medicine Statistics, Probability and Uncertainty